Why Personalization Matters

Companies are facing a challenging time today wrestling with the requirement to personalize communications and offers.  What’s more, personalization is about much more than segmentation, it’s about calibrating offers to a level of detail only achieved by understanding the consumer’s mindset and behavior at the point in time when interacting with your brand. Three articles from MarketingProfs.com discuss personalization from the challenge of accomplishing real time personalization to how to implement radical personalization and the challenges of personalization in digital marketing. Enjoy! Marketers Struggling With Real-Time Personalization Understanding the Five Pillars of Radical Personalization Marketers’ Biggest Obstacles to Effective Personalization   Tell Me What You Think! DBMCatalyst

Real-time Customer Segmentation

I’ve been around long enough to have heard the term “real-time” associated with a variety of marketing technologies, how about you?  And I often wonder about the value of that type of immediacy let alone the possibility of actually interacting in real time with my customers.  It’s always been one of those marketing aspirations which seems never quite actualized.  Or at best, it’s realized in a very mechanical way, such as re-directing a consumer to a new offer branch in a campaign based on their real-time response in a campaign.  All of which is better by far than the “blast” form of email or direct mail – which is still practiced in many industries.

In my opinion, the only value in “real-time” is knowing who your customer is in that moment as they interact with your brand.  And by “who” I really mean not only their demographics or psychographics but also their past behaviors and transactions all of which tell me something about their capacity and propensity to buy as well as their interest in/affinity towards my product.  When properly modeled the result is a score that  effectively segments your customer according to their lifetime value to your brand which enables you to deliver the most personalized, effective offer at the most profitable price.  This is real-time segmentation.

There are numerous approaches to segmentation from the simple demographic/psychographic classifications to sub-segmentations which employ sophisticated modeling techniques like factor and cluster analysis.   Ultimately most segmentation models give you a piece of the picture but even when they come close to the full definition of “who” the customer is, the result is in the “rear-view.”  The learnings  are applied after the fact, in a sense they describe who your customer “was.”   The moment of connection with your customer is lost or delayed.  The most sophisticated companies will rigorously update their customer segmentation models, but again, they are not describing the customer in “real-time” purchasing mode.

Real-time segmentation and scoring is possible today and will soon be enhancing the customer experience in not only call centers and online, but also in social and mobile channels.

Ask me about how!

Ann McCartan, DBMCatalyst

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Mini-Segmentation, what can you do?

We’ve spoken about the practice of aggregating like customers into groups or segments to better target and market to them.  And how to utilize modeling – factor and clusters – to distinguish individual characteristics within the segment they are assigned to.  These are complex segmentations built with a lot of data on each customer.

Another approach is more organic and allows the marketer to build segmentation profiles a step at a time as more data is obtained.  The good news is that content and messaging can be created at each step based on specific information gathered.  The goal is an upward journey to 1 – 1 personalization and a true 2-way conversation but these mini-segments make effective communications possible.

You don’t have to build the whole segmentation before marketing effectively to your customers.

Tell Me What You Think!

Ann McCartan, DBMCatalyst

 

Segmentation, a Deeper Look

Recently I posted a brief, basic explanation of Segmentation.  I mentioned some of the most common applications for segmenting customers for marketing purposes.  Clearly the more narrowly customers are grouped by commonalities, the more able the Marketer is to direct content and offers to meet their particular interests or behaviors.  It’s basic targeting, yes?  and the ideal outcome is always greater incremental revenue, profits and the sense by the customer that the company understands them and is providing relevant messages.   As a first step, I always look to customer characteristics and behaviors as prerequisite to defining segments.

As a second step in the process, before a customer segment is acted upon it should meet several other business-focused criteria.

  • It is possible to measure.
  • It must be large enough to earn profit.
  • It must be stable enough that it does not vanish after some time.
  • It is possible to reach potential customers via the organization’s promotion and distribution channel.
  • It is internally homogeneous (potential customers in the same segment prefer the same product qualities).
  • It is externally heterogeneous, that is, potential customers from different segments have different quality preferences.
  • It responds consistently to a given market stimulus.
  • It can be reached by market intervention in a cost-effective manner.
  • It is useful in deciding on the marketing mix.

If the business can identify distinguishable customer differences and commonalities which can be can justified from a marketing and financial perspective then solid customer segments can be derived.

Tell Me What You Think!

Ann McCartan, DBMCatalyst

 

 

Retooled and back in business!

It’s been a while since I’ve posted my thoughts on marketing automation and database marketing. After a bit of retooling of my own Nuts & Bolts, I’m back and ready to resume sharing my insights and experiences. Look for more frequent and shorter communications as well as a new focus on customer information and analysis.

Best, Ann McCartan, DBMCatalyst